Cell cycle expression heterogeneity predicts degree of differentiation.

Methods that predict fate potential or degree of differentiation from transcriptomic data have identified rare progenitor populations and uncovered developmental regulatory mechanisms. However, some state-of-the-art methods are too computationally burdensome for emerging large-scale data and all methods make inaccurate predictions in certain biological systems. We developed a method in R (stemFinder) that predicts single cell differentiation time based on heterogeneity in cell cycle gene expression. Our method is computationally tractable and is as good as or superior to competitors. As part of our benchmarking, we implemented four different performance metrics to assist potential users in selecting the tool that is most apt for their application. Finally, we explore the relationship between differentiation time and cell fate potential by analyzing a lineage tracing dataset with clonally labelled hematopoietic cells, revealing that metrics of differentiation time are correlated with the number of downstream lineages.

Microglial heterogeneity in the ischemic stroke mouse brain of both sexes.

BACKGROUND: Ischemic stroke elicits a complex and sustained immune response in the brain. Immunomodulatory treatments have long held promise for improving stroke outcomes, yet none have succeeded in the clinical setting. This lack of success is largely due to our incomplete understanding of how immune cells respond to stroke. The objective of the current study was to dissect the effect of permanent stroke on microglia, the resident immune cells within the brain parenchyma. METHODS: A permanent middle cerebral artery occlusion (pMCAO) model was used to induce ischemic stroke in young male and female mice. Microglia were sorted from fluorescence reporter mice after pMCAO or sham surgery and then subjected to single-cell RNA sequencing analysis. Various methods, including flow cytometry, RNA in situ hybridization, immunohistochemistry, whole-brain imaging, and bone marrow transplantation, were also employed to dissect the microglial response to stroke. Stroke outcomes were evaluated by infarct size and behavioral tests. RESULTS: First, we showed the morphologic and spatial changes in microglia after stroke. We then performed single-cell RNA sequencing analysis on microglia isolated from sham and stroke mice of both sexes. The data indicate no major sexual dimorphism in the microglial response to permanent stroke. Notably, we identified seven potential stroke-associated microglial clusters, including four major clusters characterized by a disease-associated microglia-like signature, a highly proliferative state, a macrophage-like profile, and an interferon (IFN) response signature, respectively. Importantly, we provided evidence that the macrophage-like cluster may represent the long-sought stroke-induced microglia subpopulation with increased CD45 expression. Lastly, given that the IFN-responsive subset constitutes the most prominent microglial population in the stroke brain, we used fludarabine to pharmacologically target STAT1 signaling and found that fludarabine treatment improved long-term stroke outcome. CONCLUSIONS: Our findings shed new light on microglia heterogeneity in stroke pathology and underscore the potential of targeting specific microglial populations for effective stroke therapies.

scRNA sequencing technology for PitNET studies.

Pituitary neuroendocrine tumors (PitNETs) are common, most likely benign tumors with complex clinical characteristics related to hormone hypersecretion and/or growing sellar tumor mass. PitNET types are classified according to their expression of specific transcriptional factors (TFs) and hormone secretion levels. Some types show aggressive, invasive, and reoccurrence behavior. Current research is being conducted to understand the molecular mechanisms regulating these high-heterogeneous neoplasms originating from adenohypophysis, and single-cell RNA sequencing (scRNA-seq) technology is now playing an essential role in these studies due to its remarkable resolution at the single-cell level. This review describes recent studies on human PitNETs performed with scRNA-seq technology, highlighting the potential of this approach in revealing these tumor pathologies, behavior, and regulatory mechanisms.

Revealing the characteristics of SETD2-mutated clear cell renal cell carcinoma through tumor heterogeneity analysis.

Background: Renal cell carcinoma (RCC) is the most prevalent type of malignant kidney tumor in adults, with clear cell renal cell carcinoma (ccRCC) comprising about 75% of all cases. The SETD2 gene, which is involved in the modification of histone proteins, is often found to have alterations in ccRCC. Yet, our understanding of how these SETD2 mutations affect ccRCC characteristics and behavior within the tumor microenvironment is still not fully understood. Methods: We conducted a detailed analysis of single-cell RNA sequencing (scRNA-seq) data from ccRCC. First, the data was preprocessed using the Python package, "scanpy." High variability genes were pinpointed through Pearson's correlation coefficient. Dimensionality reduction and clustering identification were performed using Principal Component Analysis (PCA) and the Leiden algorithm. Malignant cell identification was conducted with the "InferCNV" R package, while cell trajectories and intercellular communication were depicted using the Python packages "VIA" and "cellphoneDB." We then employed the R package "Deseq2" to determine differentially expressed genes (DEGs) between groups. Using high-dimensional weighted gene correlation network analysis (hdWGCNA), co-expression modules were identified. We intersected these modules with DEGs to establish prognostic models through univariate Cox and the least absolute shrinkage and selection operator (LASSO) method. Results: We identified 69 and 53 distinctive cell clusters, respectively. These were classified further into 12 unique cell types. This analysis highlighted the presence of an abnormal tumor sub-cluster (MT + group), identified by high mitochondrial-encoded protein gene expression and an indication of unfavorable prognosis. Investigation of cellular interactions spotlighted significant interactions between the MT + group and endothelial cells, macrophaes. In addition, we developed a prognostic model based on six characteristic genes. Notably, risk scores derived from these genes correlated significantly with various clinical features. Finally, a nomogram model was established to facilitate more accurate outcome prediction, incorporating four independent risk factors. Conclusion: Our findings provide insight into the crucial transcriptomic characteristics of ccRCC associated with SETD2 mutation. We discovered that this mutation-induced subcluster could stimulate M2 polarization in macrophages, suggesting a heightened propensity for metastasis. Moreover, our prognostic model demonstrated effectiveness in forecasting overall survival for ccRCC patients, thus presenting a valuable clinical tool.

Single-cell RNA sequencing in diabetic kidney disease: a literature review.

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), and its pathogenesis has not been clarified. Current research suggests that DKD involves multiple cell types and extra-renal factors, and it is particularly important to clarify the pathogenesis and identify new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) technology is high-throughput sequencing of the transcriptomes of individual cells at the single-cell level, which is an effective technology for exploring the development of diseases by comparing genetic information, reflecting the differences in genetic information between cells, and identifying different cell subpopulations. Accumulating evidence supports the role of scRNA-seq in revealing the pathogenesis of diabetes and strengthening our understanding of the molecular mechanisms of DKD. We reviewed the scRNA-seq data this time. Then, we analyzed and discussed the applications of scRNA-seq technology in DKD research, including annotation of cell types, identification of novel cell types (or subtypes), identification of intercellular communication, analysis of cell differentiation trajectories, gene expression detection, and analysis of gene regulatory networks, and lastly, we explored the future perspectives of scRNA-seq technology in DKD research.

Identification of CGNL1 as a diagnostic marker in fibroblasts of diabetic foot ulcers: Insights from single cell RNA sequencing and bulk sequencing data.

OBJECTIVES: This study aimed to explore the unique transcriptional feature of fibroblasts subtypes and the role of ferroptosis in diabetic foot ulcers (DFUs). METHODS: The GEO (Gene Expression Omnibus) was searched to obtain the DFUs single-cell and transcriptional datasets. After identifying cell types by classic marker genes, the integrated single-cell dataset was used to run trajectory inference, RNA velocity, and ligand-receptor interaction analysis. Next, bulk RNA-seq datasets of DFUs were analyzed to the key ferroptosis genes. RESULTS: Here, we profile 83529 single transcriptomes from the foot samples utilizing single-cell sequencing (scRNA-seq) data of DFU from GEO database and identified 12 cell types, with fibroblasts exhibiting elevated levels of ferroptosis activity and substantial cellular heterogeneity. Our results defined six main fibroblast subsets that showed mesenchymal, secretory-reticular, secretory-papillary, pro-inflammatory, myogenesis, and healing-enriched functional annotations. Trajectory inference and cell-cell communication analysis revealed two major cell fates with subpopulations of fibroblasts and altered ligand-receptor interactions. Bulk RNA sequencing data identified CGNL1 as a distinctive diagnostic signature in fibroblasts. Notably, CGNL1 positively correlated with pro-inflammatory fibroblasts. CONCLUSIONS: Overall, our analysis delineated the heterogeneity present in cell populations of DFUs, showing distinct fibroblast subtypes characterized by their own unique transcriptional features and enrichment functions. Our study will help us better understand DFUs pathogenesis and identifies CGNL1 as a potential target for DFUs therapies.

Establishment and transcriptome analysis of single blastomere-derived cell lines from zebrafish.

Maintaining chromosome euploidy in zebrafish embryonic cells is challenging because of the degradation of genomic integrity during cell passaging. In this study, we report the derivation of zebrafish cell lines from single blastomeres. These cell lines have a stable chromosome status attributed to BMP4 and exhibit continuous proliferation in vitro. Twenty zebrafish cell lines are successfully established from single blastomeres. Single-cell transcriptome sequencing analysis confirms the fidelity of gene expression profiles throughout long-term culturing of at least 45 passages. The long-term cultured cells are specialized into epithelial cells, exhibiting similar expression patterns validated by integrative transcriptomic analysis. Overall, this work provides a protocol for establishing zebrafish cell lines from single blastomeres, which can serve as valuable tools for in vitro investigations of epithelial cell dynamics in terms of life-death balance and cell fate determination during normal homeostasis.

A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset.

Cell-type annotation is a critical step in single-cell data analysis. With the development of numerous cell annotation methods, it is necessary to evaluate these methods to help researchers use them effectively. Reference datasets are essential for evaluation, but currently, the cell labels of reference datasets mainly come from computational methods, which may have computational biases and may not reflect the actual cell-type outcomes. This study first constructed an experimentally labeled immune cell-subtype single-cell dataset of the same batch and systematically evaluated 18 cell annotation methods. We assessed those methods under five scenarios, including intra-dataset validation, immune cell-subtype validation, unsupervised clustering, inter-dataset annotation, and unknown cell-type prediction. Accuracy and ARI were evaluation metrics. The results showed that SVM, scBERT, and scDeepSort were the best-performing supervised methods. Seurat was the best-performing unsupervised clustering method, but it couldn't fully fit the actual cell-type distribution. Our results indicated that experimentally labeled immune cell-subtype datasets revealed the deficiencies of unsupervised clustering methods and provided new dataset support for supervised methods.

Single-cell Analysis of Debrided Diabetic Foot Ulcers Reveals Dysregulated Wound Healing Environment in non-Hispanic Blacks.

Diabetic foot ulcer (DFU) is a critical complication of diabetes, but the wound microenvironment and its healing process are not completely understood. In this study, we optimized single-cell profiling from sharp debrided ulcers. Our findings demonstrate that healing-DFUs were significantly enriched with distinct fibroblasts expressing genes related to inflammation (CHI3L1, IL6) and extracellular matrix remodeling (ASPN), validating our previous studies on surgically resected ulcers. The race-focused analysis depicted lower expression of key healing-associated genes such as CHIL3L1, MMP11, and SFRP4 in fibroblasts of non-Hispanic Black (NHB) patients compared to White patients. In cellular communication analysis, healing enriched fibroblasts of NHBs exhibited upregulation of signaling pathways such as WNT while those of White showed IGF and MK pathways upregulation. Our findings advocate race as a risk marker of DFU outcomes, likely reflecting underlying disparities in environmental exposures and access to care that profoundly influence healing markers. Using sharp debrided tissues for single-cell assays, this study highlights the need for in-depth investigations into dysregulated wound healing microenvironments of under-represented racial groups.

Gene Regulatory Network Analysis of Decidual Stromal Cells and Natural Killer Cells.

Human reproductive success relies on the proper differentiation of the uterine endometrium to facilitate implantation, formation of the placenta, and pregnancy. This process involves two critical types of decidual uterine cells: endometrial/decidual stromal cells (dS) and uterine/decidual natural killer (dNK) cells. To better understand the transcription factors governing the in vivo functions of these cells, we analyzed single-cell transcriptomics data from first-trimester terminations of pregnancy, and for the first time conducted gene regulatory network analysis of dS and dNK cell subpopulations. Our analysis revealed stromal cell populations that corresponded to previously described in vitro decidualized cells and senescent decidual cells. We discovered new decidualization driving transcription factors of stromal cells for early pregnancy, including DDIT3 and BRF2, which regulate oxidative stress protection. For dNK cells, we identified transcription factors involved in the immunotolerant (dNK1) subpopulation, including IRX3 and RELB, which repress the NFKB pathway. In contrast, for the less immunotolerant (dNK3) population we predicted TBX21 (T-bet) and IRF2-mediated upregulation of the interferon pathway. To determine the clinical relevance of our findings, we tested the overrepresentation of the predicted transcription factors target genes among cell type-specific regulated genes from pregnancy disorders, such as recurrent pregnancy loss and preeclampsia. We observed that the predicted decidualized stromal and dNK1-specific transcription factor target genes were enriched with the genes downregulated in pregnancy disorders, whereas the predicted dNK3-specific targets were enriched with genes upregulated in pregnancy disorders. Our findings emphasize the importance of stress tolerance pathways in stromal cell decidualization and immunotolerance promoting regulators in dNK differentiation.

Single-cell transcriptomic sequencing analysis of mechanistic insights into the IFN-γ signaling pathway in different tumor cells.

PURPOSE: This study aimed to investigate the relationship between the interferon-gamma (IFN-γ) pathway in different tumor microenvironments (TME) and patients' prognosis, as well as the regulatory mechanisms of this pathway in tumor cells. METHODS: Using RNA-seq data from the TCGA database, we analyzed the predictive value of the IFN-γ pathway across various tumors. We employed a univariate Cox regression model to assess the prognostic significance of IFN-γ signaling in different tumor types. Additionally, we analyzed single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database to examine the distribution characteristics of the IFN-γ pathway and explore its regulatory mechanisms, highlighting how IFN-γ influenced cellular interactions within the TME. RESULTS: Our analysis revealed a significant association between the IFN-γ pathway and adverse prognosis in pan-cancer tissues (P < 0.001). Interestingly, this correlation varied regarding positive and negative regulation across different tumor types. Through a detailed examination of scRNA-seq data, we found that the IFN-γ pathway exerted substantial regulatory effects on stromal and immune cells. In contrast, its expression and regulatory patterns in tumor cells exhibited diversity and heterogeneity. Further analysis indicated that the IFN-γ pathway not only enhanced the immunogenicity of tumor cells but also inhibited their proliferation. Cell-cell interaction analysis confirmed the pivotal role of the IFN-γ pathway within the overall regulatory network. Moreover, we identified HMGB2 (high mobility group box 2) in T cells as a potential key regulator of tumor cell proliferation. CONCLUSIONS: The IFN-γ pathway exhibited a dual function by both suppressing tumor cell proliferation and enhancing their immunogenicity, positioning it as a pivotal target for refined cancer diagnosis and cancer strategies.

Molecular and network disruptions in neurodevelopment uncovered by single cell transcriptomics analysis of CHD8 heterozygous cerebral organoids.

More than 100 genes have been associated with significantly increased risks of autism spectrum disorders (ASD) with an estimate of ∼1000 genes that may contribute. The new challenge is to investigate the molecular and cellular functions of these genes during neural and brain development, and then even more challenging, to link the altered molecular and cellular phenotypes to the ASD clinical manifestations. In this study, we used single-cell RNA-seq analysis to study one of the top risk genes, CHD8, in cerebral organoids, which models early neural development. We identified 21 cell clusters in the organoid samples, representing non-neuronal cells, neural progenitors, and early differentiating neurons at the start of neural cell fate commitment. Comparisons of the cells with one copy of a CHD8 knockout allele, generated by CRISPR/Cas9 editing, and their isogenic controls uncovered thousands of differentially expressed genes, which were enriched with functions related to neural and brain development, cilium organization, and extracellular matrix organization. The affected genes were also enriched with genes and pathways previously implicated in ASD, but surprisingly not for schizophrenia and intellectual disability risk genes. The comparisons also uncovered cell composition changes, indicating potentially altered neural differential trajectories upon CHD8 reduction. Moreover, we found that cell-cell communications were affected in the CHD8 knockout organoids, including the interactions between neural and glial cells. Taken together, our results provide new data and information for understanding CHD8 functions in the early stages of neural lineage development and interaction.

CD36 restricts lipid-associated macrophages accumulation in white adipose tissues during atherogenesis.

Visceral white adipose tissues (WAT) regulate systemic lipid metabolism and inflammation. Dysfunctional WAT drive chronic inflammation and facilitate atherosclerosis. Adipose tissue-associated macrophages (ATM) are the predominant immune cells in WAT, but their heterogeneity and phenotypes are poorly defined during atherogenesis. The scavenger receptor CD36 mediates ATM crosstalk with other adipose tissue cells, driving chronic inflammation. Here, we combined the single-cell RNA sequencing technique with cell metabolic and functional assays on major WAT ATM subpopulations using a diet-induced atherosclerosis mouse model (Apoe-null). We also examined the role of CD36 using Apoe/Cd36 double-null mice. Based on transcriptomics data and differential gene expression analysis, we identified a previously undefined group of ATM displaying low viability and high lipid metabolism and labeled them as "unhealthy macrophages". Their phenotypes suggest a subpopulation of ATM under lipid stress. We also identified lipid-associated macrophages (LAM), which were previously described in obesity. Interestingly, LAM increased 8.4-fold in Apoe/Cd36 double-null mice on an atherogenic diet, but not in Apoe-null mice. The increase in LAM was accompanied by more ATM lipid uptake, reduced adipocyte hypertrophy, and less inflammation. In conclusion, CD36 mediates a delicate balance between lipid metabolism and inflammation in visceral adipose tissues. Under atherogenic conditions, CD36 deficiency reduces inflammation and increases lipid metabolism in WAT by promoting LAM accumulation.

An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data.

The increasing emergence of the time-series single-cell RNA sequencing (scRNA-seq) data, inferring developmental trajectory by connecting transcriptome similar cell states (i.e., cell types or clusters) has become a major challenge. Most existing computational methods are designed for individual cells and do not take into account the available time series information. We present IDTI based on the Increment of Diversity for Trajectory Inference, which combines time series information and the minimum increment of diversity method to infer cell state trajectory of time-series scRNA-seq data. We apply IDTI to simulated and three real diverse tissue development datasets, and compare it with six other commonly used trajectory inference methods in terms of topology similarity and branching accuracy. The results have shown that the IDTI method accurately constructs the cell state trajectory without the requirement of starting cells. In the performance test, we further demonstrate that IDTI has the advantages of high accuracy and strong robustness.

Single-cell RNA sequencing reveals the CRTAC1+ population actively contributes to the pathogenesis of spinal ligament degeneration by SPP1+ macrophage.

Degenerative spinal stenosis is a chronic disease that affects the spinal ligaments and associated bones, resulting in back pain and disorders of the limbs among the elderly population. There are few preventive strategies for such ligament degeneration. We here aimed to establish a comprehensive transcriptomic atlas of ligament tissues to identify high-priority targets for pharmaceutical treatment of ligament degeneration. Here, single-cell RNA sequencing was performed on six degenerative ligaments and three traumatic ligaments to understand tissue heterogeneity. After stringent quality control, high-quality data were obtained from 32,014 cells. Distinct cell clusters comprising stromal and immune cells were identified in ligament tissues. Among them, we noted that collagen degradation associated with CTHRC1+ fibroblast-like cells and calcification linked to CRTAC1+ chondrocyte-like cells were key features of ligament degeneration. SCENIC analysis and further experiments identified ATF3 as a key transcription factor regulating the pathogenesis of CRTAC1+ chondrocyte-like cells. Typically, immune cells infiltrate localized organs, causing tissue damage. In our study, myeloid cells were found to be inflammatory-activated, and SPP1+ macrophages were notably enriched in degenerative ligaments. Further exploration via CellChat analysis demonstrated a robust interaction between SPP1+ macrophages and CRTAC1+ chondrocyte-like cells. Activated by SPP1, ATF3 propels the CRTAC1/MGP/CLU axis, fostering ligament calcification. Our unique resource provides novel insights into possible mechanisms underlying ligament degeneration, the target cell types, and molecules that are expected to mitigate degenerative spinal ligament. We also highlight the role of immune regulation in ligament degeneration and calcification, enhancing our understanding of this disease.

Identification and analysis of significant genes in nonalcoholic steatohepatitis-hepatocellular carcinoma transformation: Bioinformatics analysis and machine learning approach.

PURPOSE: Nonalcoholic steatohepatitis (NASH) has been an increasingly significant contributor to hepatocellular carcinoma (HCC). Understanding the progression from NASH to HCC is critical to early diagnosis and elucidating the underlying mechanisms. RESULTS: 5 significant prognostic genes related to NASH-HCC transformation were identified through algorithm selection, which were ME1, TP53I3, SOCS2, GADD45G and CYP7A1. A diagnostic model for NASH prediction was established (AUC=0.988). TP53I3 and SOCS2 were selected as potential critical genes in the progression of NASH-HCC by external dataset validation and in vitro experiments on NASH and HCC cell lines. Immune infiltration analysis illustrated the correlation between 5 significant prognostic genes and immune cells. Single-cell analysis identified hepatocytes related to NASH-HCC transformation markers, revealing their promoting role in the transformation from NASH to HCC. CONCLUSION: With bulk-seq analysis and single-cell analysis, 5 significant prognostic genes related to NASH-HCC transformation were identified and validated at both dataset and in vitro experiment level. Among them, TP53I3 and SOCS2 might be potential critical genes in NASH-HCC progression. Single-cell analysis identified and revealed the critical role that NASH-HCC related hepatocytes play in NASH-HCC tansformation. Our research may introduce a new perspective to the diagnosis, treatment of NASH-related HCC.

Regulator of G-protein signaling expression in human intestinal enteroendocrine cells and potential role in satiety hormone secretion in health and obesity.

BACKGROUND: Gut L-type enteroendocrine cells (EECs) are intestinal chemosensory cells that secrete satiety hormones GLP-1 and PYY in response to activation of G-protein coupled receptors (GPCRs) by luminal components of nutrient digestion and microbial fermentation. Regulator of G-protein Signaling (RGS) proteins are negative regulators of GPCR signaling. The expression profile of RGS in EECs, and their potential role in satiety hormone secretion and obesity is unknown. METHODS: Transcriptomic profiling of RGS was completed in native colonic EECs was completed using single-cell RNA sequencing (scRNA-Seq) in lean and obesity, and human jejunal EECs with data obtained from a publicly available RNAseq dataset (GSE114853). RGS validation studies were completed using whole mucosal intestinal tissue obtained during endoscopy in 61 patients (n = 42 OB, n = 19 Lean); a subset of patients' postprandial plasma was assayed for GLP-1 and PYY. Ex vivo human intestinal cultures and in vitro NCI-H716 cells overexpressing RGS9 were exposed to GLP-1 secretagogues in conjunction with a nonselective RGS-inhibitor and assayed for GLP-1 secretion. FINDINGS: Transcriptomic profiling of colonic and jejunal enteroendocrine cells revealed a unique RGS expression profile in EECs, and further within GLP-1+ L-type EECs. In obesity the RGS expression profile was altered in colonic EECs. Human gut RGS9 expression correlated positively with BMI and negatively with postprandial GLP-1 and PYY. RGS inhibition in human intestinal cultures increased GLP-1 release from EECs ex vivo. NCI-H716 cells overexpressing RGS9 displayed defective nutrient-stimulated GLP-1 secretion. INTERPRETATION: This study introduces the expression profile of RGS in human EECs, alterations in obesity, and suggests a role for RGS proteins as modulators of GLP-1 and PYY secretion from intestinal EECs. FUNDING: AA is supported by the NIH(C-Sig P30DK84567, K23 DK114460), a Pilot Award from the Mayo Clinic Center for Biomedical Discovery, and a Translational Product Development Fund from The Mayo Clinic Center for Clinical and Translational Science Office of Translational Practice in partnership with the University of Minnesota Clinical and Translational Science Institute.

Exploring the prognostic value of BRMS1 + microglia based on single-cell anoikis regulator patterns in the immunologic microenvironment of GBM.

BACKGROUND: Anoikis is a specialized form of programmed cell death induced by the loss of cell adhesion to the extracellular matrix (ECM). Acquisition of anoikis resistance is a significant marker for cancer cell invasion, metastasis, therapy resistance, and recurrence. Although current research has identified multiple factors that regulate anoikis resistance, the pathological mechanisms of anoikis-mediated tumor microenvironment (TME) in glioblastoma (GBM) remain largely unexplored. METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) data and employing non-negative matrix factorization (NMF), we identified and characterized TME cell clusters with distinct anoikis-associated gene signatures. Prognostic and therapeutic response analyses were conducted using TCGA and CGGA datasets to assess the clinical significance of different TME cell clusters. The spatial relationship between BRMS1 + microglia and tumor cells was inferred from spatial transcriptome RNA sequencing (stRNA-seq) data. To simulate the tumor immune microenvironment, co-culture experiments were performed with microglia (HMC3) and GBM cells (U118/U251), and microglia were transfected with a BRMS1 overexpression lentivirus. Western blot or ELISA were used to detect BRMS1, M2 macrophage-specific markers, PI3K/AKT signaling proteins, and apoptosis-related proteins. The proliferation and apoptosis capabilities of tumor cells were evaluated using CCK-8, colony formation, and apoptosis assays, while the invasive and migratory abilities of tumor cells were assessed using Transwell assays. RESULTS: NMF-based analysis successfully identified CD8 + T cell and microglia cell clusters with distinct gene signature characteristics. Trajectory analysis, cell communication, and gene regulatory network analyses collectively indicated that anoikis-mediated TME cell clusters can influence tumor cell development through various mechanisms. Notably, BRMS1 + AP-Mic exhibited an M2 macrophage phenotype and had significant cell communication with malignant cells. Moreover, high expression of BRMS1 + AP-Mic in TCGA and CGGA datasets was associated with poorer survival outcomes, indicating its detrimental impact on immunotherapy. Upregulation of BRMS1 in microglia may lead to M2 macrophage polarization, activate the PI3K/AKT signaling pathway through SPP1/CD44-mediated cell interactions, inhibit tumor cell apoptosis, and promote tumor proliferation and invasion. CONCLUSION: This pioneering study used NMF-based analysis to reveal the important predictive value of anoikis-regulated TME in GBM for prognosis and immunotherapeutic response. BRMS1 + microglial cells provide a new perspective for a deeper understanding of the immunosuppressive microenvironment of GBM and could serve as a potential therapeutic target in the future.

GJB2 Promotes HCC Progression by Activating Glycolysis Through Cytoplasmic Translocation and Generating a Suppressive Tumor Microenvironment Based on Single Cell RNA Sequencing.

Despite substantial breakthroughs in the treatment of hepatocellular carcinoma (HCC) in recent years, many patients are diagnosed in the middle or late stages, denying them the option for surgical excision. Therefore, it is of great importance to find effective therapeutic targets of HCC. In this study, it is found that Gap junction protein beta-2 (GJB2) is highly enriched in malignant cells based on single-cell RNA sequencing and higher expression of GJB2 indicates a worse prognosis. The localization of GJB2 in HCC cancer cells is changed compared with normal liver tissue. In cancer cells, GJB2 tends to be located in the cytoplasm and nucleus, while in normal tissues, GJB2 is mainly located on the cell membrane. GJB2 is related to glycolysis, promoting NF-κB pathway via inducing the ubiquitination degradation of IκBa, and activating HIF-1α/GLUT-1/PD-L1 pathway. In addition, GJB2 knockdown reshapes tumor immune microenvironment and Salvianolic acid B inhibits the activity of GJB2. In conclusion, GJB2 promotes HCC progression by activating glycolysis through cytoplasmic translocation and generating a suppressive tumor microenvironment. Salvianolic acid B inhibits the expression of GJB2 and enhances the sensitivity of anti-PD1 therapy, which may provide insights into the development of novel combination therapeutic strategies for HCC.

Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise.

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome-wide noise, remains unclear. Here we utilize a small-molecule perturbation (IdU) to amplify noise and assess noise quantification from numerous scRNA-seq algorithms on human and mouse datasets, and then compare to noise quantification from single-molecule RNA FISH (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise, without altered mean-expression levels, for ~90% of genes and that smFISH analysis verifies noise amplification for the vast majority of genes tested. Collectively, the analyses suggest that most scRNA-seq algorithms are appropriate for quantifying noise including a simple normalization approach, although all of these systematically underestimate noise compared to smFISH. From a practical standpoint, this analysis argues that IdU is a globally penetrant noise-enhancer molecule-amplifying noise without altering mean-expression levels-which could enable investigations of the physiological impacts of transcriptional noise.